Spss statistical package version 25.0 for windows

SPSS statistical package version 25.0 for Windows (SPSS, Inc., Chicago, IL, USA) was used to perform all statistical analyses. Means and standard deviations were computed for all variables, and normality was checked using the Shapiro–Wilk test. Non-normal data were converted using square root (saliva flow rate) or logarithmic (concentration and secretion rate of stress hormones) transformations that achieved normality for all variables. The participants’ stress hormones (cortisol and SIgA) were further analyzed for significant differences among the groups using a one-way ANOVA. Pearson correlation coefficients analysis and a stepwise multiple regression analysis were conducted to examine the effect of the level of stress hormones on anxiety. Post hoc analysis (Tukey test) was used to compare specific differences when significance was found. Statistical significance was accepted at the 0.05 level.

The SPSS statistical package version 25.0 for Windows (SPSS, Inc., Chicago, IL, USA) was used to perform all statistical evaluations. Means and standard deviations were computed for all variables. Binary logistic regression analyses were performed to examine the independent and joint associations of sufficient MVPA level with MetS risk factors. The odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for these associations. In the analysis of the joint association, the group that met the recommended MVPA level (150 min/wk) was regarded as the reference group. MetS risk factors were further analyzed for significant difference among the aged groups using a One-Way ANOVA, and Post-hoc analysis (Bonferroni) was used to compare specific differences when significance was found. All statistical significance was accepted at the 0.05 level.

Statistical analyses were performed using SPSS statistical package version 25.0 for Windows (SPSS Inc., Chicago, IL, USA). The Hardy–Weinberg equilibrium for both genotypes was accessed using Pearson’s χ2 test. Comparisons between genotypes and severity groups (severe versus non-severe), and injury per season were made using the χ2 test and Fisher’s Exact Test. SNP stats (https://snpstats.net accessed on 24 October 2022) was used to verify the Akaike Information Criterion (AIC) for the most suitable genetic model. Binary logistic regression was performed to verify whether the ACTN3 R577X genotypes and the ACE I/D recessive model are predictors of the development of severe injury, creating a model and calculating the odds ratio (OR) and 95% confidence intervals (CI). The interaction between the polymorphisms and injury per season was assessed with Analysis of Covariance (ANCOVA) adjusted by weight and age. p-values < 0.05 were considered statistically significant.